Juing-Shian Chiou

Attitude Control of a Single Tilt Tri-Rotor UAV System: Dynamic Modeling and Each Channel's Nonlinear Controllers Design

This paper has implemented nonlinear control strategy for the single tilt tri-rotor aerial robot. Based on Newton-Euler’s laws, the linear and nonlinear mathematical models of tri-rotor UAVs are obtained. A numerical analysis using Newton-Raphson method is chosen for finding hovering equilibrium point. Back-stepping nonlinear controller design is based on constructing Lyapunov candidate function for closed-loop system. By imitating the linguistic logic of human thought, fuzzy logic controllers (FLCs) are designed based on control rules and membership functions, which are much less rigid than the calculations computers generally perform. Effectiveness of the controllers design scheme is shown through nonlinear simulation model on each channel.

Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

This paper incorporates principal component analysis (PCA) with support vector machine-particle swarm optimization (SVM-PSO) for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design

Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO)-based algorithm and the evolutionary programming (EP) algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models.

Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control

In this article, the particle swarm optimization algorithm is presented using an improved stochastic variant strategy to optimize the gains of the fuzzy proportional–integral–derivative controller. The particle swarm optimization algorithm aims to renew the elite parameters for the schemes of fuzzy proportional–integral–derivative controls. The integral of time multiplied by absolute error criterion is used to estimate the system performance due to saving the setting and operation time. Facing nonlinear quadrotor attitude control models, the results demonstrate the proposed scheme with better performance.

Design Hybrid Evolutionary PSO Aiding Miniature Aerial Vehicle Controllers

Based on some sort of simplified fuzzy reasoning methods and PID parameters, many fuzzy-PID controller schemes are applied to control the complicated systems, recently. Mathematical optimization calculations are making in encouraging EP with PSO yielding a higher-quality solution. In this paper, a novel HEPSO algorithm as an improved variant of stochastic optimization strategy PSO, which assigns optimization to fuzzy-PID control gains, is established. The benefit integration design of the HEPSO algorithm structure is generating and updating the new parameters for the fuzzy-PID control schemes in the short setting and operation time. The proposed controllers have demonstrated performance to MAV models.

Fast object detection for human-robot interaction control

This paper proposes a fast object detection algorithm based on structural light analysis, which is used to detect and recognize human gesture and pose and then to conclude the respective commands for human-robot interaction control. The RGB camera and the infrared light module aim to do distance estimation of a body or several bodies. The module not only provides image perception but also objective skeleton detection. In which, a laser source in the infrared light module emits invisible infrared light which passes through a filter and is scattered into a semi-random but constant pattern of small dots which is projected onto the environment in front of the sensor. The reflected pattern is then detected by an infrared camera and analyzed for depth estimation. Since the depth of object is a key parameter for pose recognition, one can estimate the distance to each dot and then get depth information by calculation of distance between emitter and receiver. In this paper, the human poses are estimated and analyzed by the proposed scheme, and then the resultant data concluded by the fuzzy decision making system are used to launch respective robotic motions. The experimental results demonstrate the feasibility of the proposed system.

Robust Delay Independent Stability Analysis for the Switched Interval Time-Delay Systems with Time-Driven Switching Strategy

Some new criteria of delay independent stability for the switched interval time-delay systems are deduced. The switching structure does depend on time-driven switching strategies. The total activation time ratio of the switching law can be determined to guarantee that the switched interval time-delay system is exponentially stable.

Design of Hybrid Based Image Recognition System for Intelligent Interactive Robots

This paper proposed the image recognition system that integrates principal component analysis (PCA) and support vector machine (SVM) for intelligent robots. The integrated scheme aims to apply the SVM method to improve the validity of PCA based image recognition systems on dynamic robotic visual perception. Experimental results show that the proposed method simplifies features effectively and obtains a higher classification accuracy.